Covariance matrix reconstruction with iterative mismatch approximation for robust adaptive beamforming

Duan, Yanliang; Zhang, Shunlan; Cao, Weihua

doi:10.1080/09205071.2021.1952901

Cited by 2 publications

(17 citation statements)

References 27 publications

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“…For estimating the SOI SV, we employed the iterative mismatch approximation method proposed in [ 29 ] to correct the presumed SOI SV. The iterative mismatch approximation method depends on searching for the SV mismatch in the margin of the amplitude and phase error.…”

Section: Proposed Methodsmentioning

confidence: 99%

“…For estimating the SOI SV, we employed the iterative mismatch approximation method proposed in [29] to correct the presumed SOI SV. The iterative mismatch approx-…”

Section: Soi Sv Estimation and Beamformer Weight Vector Calculationmentioning

confidence: 99%

“…Furthermore, the residual noise power was considered to improve the estimation accuracy of incident signal power in [ 28 ]. In [ 29 ], the iterative mismatch approximation method was employed to estimate the power and SV of all incident signals; then, these estimates were used to reconstruct the INCM. In [ 30 ], all nominal SVs were adjusted to an accurate version by a line search along the corresponding gradient vector.…”

Section: Introductionmentioning

confidence: 99%

“…Subsequently, the INCM was reconstructed by replacing the eigenvector columns of the SCM, which can span to the signal subspace with the corresponding eigenvectors of the projection matrix. Finally, the SOI SV was estimated by employing the iterative mismatch approximation method presented in [ 29 ]. The theoretical analysis and simulation results demonstrated that the proposed method can efficiently deal with multiple types of mismatches.…”

Section: Introductionmentioning

confidence: 99%

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Low-Complexity Robust Adaptive Beamforming Based on INCM Reconstruction via Subspace Projection

Duan

Mei

et al. 2021

Sensors

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Adaptive beamforming is sensitive to steering vector (SV) and covariance matrix mismatches, especially when the signal of interest (SOI) component exists in the training sequence. In this paper, we present a low-complexity robust adaptive beamforming (RAB) method based on an interference–noise covariance matrix (INCM) reconstruction and SOI SV estimation. First, the proposed method employs the minimum mean square error criterion to construct the blocking matrix. Then, the projection matrix is obtained by projecting the blocking matrix onto the signal subspace of the sample covariance matrix (SCM). The INCM is reconstructed by replacing part of the eigenvector columns of the SCM with the corresponding eigenvectors of the projection matrix. On the other hand, the SOI SV is estimated via the iterative mismatch approximation method. The proposed method only needs to know the priori-knowledge of the array geometry and angular region where the SOI is located. The simulation results showed that the proposed method can deal with multiple types of mismatches, while taking into account both low complexity and high robustness.

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Section: Proposed Methodsmentioning

confidence: 99%

“…For estimating the SOI SV, we employed the iterative mismatch approximation method proposed in [29] to correct the presumed SOI SV. The iterative mismatch approx-…”

Section: Soi Sv Estimation and Beamformer Weight Vector Calculationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Low-Complexity Robust Adaptive Beamforming Based on INCM Reconstruction via Subspace Projection

Duan

Mei

et al. 2021

Sensors

Self Cite

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“…The Standard Capon Beamformer (SCB) is an optimal spatial filter that maximizes the array output signal to the interference-plus-noise ratio (SINR), provided that the true covariance matrix and the signal steering vector are accurately known [2]. However, the existence of systematic model mismatch, such as array calibration error, finite snapshots, and others, is inevitable [1,3]. Adaptive beamformers are sensitive to model mismatch, especially when the desired signal is present in the sampling sequences [4].…”

Section: Introductionmentioning

confidence: 99%

Oblique Projection-Based Covariance Matrix Reconstruction and Steering Vector Estimation for Robust Adaptive Beamforming

et al. 2022

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Adaptive beamforming can efficiently contract interference and noise. Due to high sensitivity of the beamformer to model mismatch, the capability of interference reduction will critically degrade when the signal model mismatch occurs, particularly when the sampling sequence contains the desired signal. For the purpose of enhancing the robustness of beamformers to signal model mismatch, we propose a new robust adaptive beamforming (RAB) method. Firstly, the precise steering vector (SV) associating with the desired signal is estimated by employing the minimum norm of subspace projection (MNSP) approach. Secondly, the nominal interference SVs are estimated via the maximum entropy power spectrum. Subsequently, the corrected interference SVs and powers are obtained by oblique projection. Finally, the interference-plus-noise covariance matrix (INCM) is reconstructed, and the proposed RAB is obtained. Multiple simulations are carried out and demonstrate the robustness of the proposed RAB method.

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Covariance matrix reconstruction with iterative mismatch approximation for robust adaptive beamforming

Cited by 2 publications

References 27 publications

Low-Complexity Robust Adaptive Beamforming Based on INCM Reconstruction via Subspace Projection

Low-Complexity Robust Adaptive Beamforming Based on INCM Reconstruction via Subspace Projection

Oblique Projection-Based Covariance Matrix Reconstruction and Steering Vector Estimation for Robust Adaptive Beamforming

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